The PREDICT Project: Enhancing DDDAS/Infosymbiotics Systems with

Privacy and Security

Li Xiong and Vaidy SunderamStudents: Layla Pournajaf, Daniel Garcia-Ulloa, Xiaofeng Xu

Dept. of Math and Computer ScienceEmory University

INFORMS 2015, Philadelphia, PA, 3 November 2015

AFOSR DDDAS FA9550-12-1-0240

DDDAS as a Unifying Paradigm

• Ability to dynamically integrate generated data into

an application; feedback loop to steer measurement • Acquisition – measurements, streams, databases

• Assimilation – preprocessing, aggregation, fusion

• Analytics – simulations, decisions, knowledge discovery

• Action – incorporate new results, feedback to above

• Platforms & Domains • Internet of Things (IoT), Smart(er) Systems

• Physical, chemical, biological, engineering, weather

• Medical, health, transport, infrastructure, military, disaster

• Trends: InfoSymbiotics – Big data and Big computing

• Evolution: ubiquitous sensing/informatics/multimodal

From the Sensor-Scale to the Exa-Scale

• Hierarchical DDDAS

• Devices • Embedded devices

• Sensors

• UAV/UGV

• Participants

• Regional/Central • HPC Clusters

• Exascale machines

• Data/knowledge

bases

• Networking

Multilevel DDDAS Systems

• End-to-end data/compute/control flow & interaction

*Original figure due to Dr. Frederica Darema

Next Generation DDDAS/Infosymbiotics Systems

• Participant/data privacy

• Identity, location and data are all sensitive

• Uncertainty

• Measurements/observations subject to error

• At exascale, intermittent failures are inevitable

• Cloaking/obfuscation for privacy

• Handle privacy & uncertainty within unified rubric

• Aggregation, fusion and summarization

• Transformations in the presence of uncertainty

• Secure high-performance multiparty computation

• At each DDDAS level, perform local computations and

analytics, cooperatively with mutually untrusted peers

Foundational Work

• Privacy Preserving Data Collection with Feedback Control

• Privacy Preserving Data Aggregation with Feedback Control

• Secure Data Collection and Aggregation

Privacy Preserving Data Collection

Privacy Preserving Data Aggregation

Data Modeling Sensitive Data

Streams

Aggregated

Data streams

Data Contributors Trusted Aggregator

Privacy Preserving Feedback Control

Application

Aggregation

Perturbation

Prediction

Correction

Cloaking

Collection

Next Generation DDDAS

• Privacy-preserving, secure acquisition High-performance

• Fusion/aggregation of uncertain data secure distr. comp.

• Prediction/correction/application steering + feedback loop

}

Privacy Preserving Participant Management

• Feedback-controlled assignment of

cloaked mobile participants to targets Task management feedback

Measurement feedback

Input/steering data

• Challenges: maximize coverage, minimize

cost; handle mobile participants/targets

DDDAS Secure Tasking

Mobile participants/sensors: feedback + prediction

a) Exact Trajectories b) Uncertain Trajectories

Predictive/Corrective scheme

augmented with mobility model

Model:

Meas:

Pred:

Update:

Xt ∼ p(Xt | Xt−1) Zt ∼ p(Zt | Xt) Z1:t = Z1, . . . , Zt

p(Xt | Z1:t−1) = Σ p(Xt | Xt−1) p(Xt−1 | Z1:t−1)

p(Xt | Z1:t) =

p(Yt | Xt) p(Xt | Z1:t−1)

Σ p(Yt | Xt) p(Xt | Z1:t−1)

DDDAS Enhanced Cloaked Tasking

• TC: Sum of participant to assigned-target distancesTU: Sum of valid assignments/target normalized to requiredPC: Penalized cost – sum of cost + uncovered penalty

Data Assimilation under Uncertainty

• Objective: Aggregation/fusion of unreliable

observations for analytics/decision-making

• Spatio-temporal crowdsensing example:

• M participants (unreliably) report about

• N events at one or more of R consecutive times

• Observations ∈ S = {s1, s2, … sv} or ∅ (missing)

• Determine “state label” at location lj at time tk

Truth Inference Approach

• Hidden Markov Model using iterative approach to

determine transition probabilities

• Challenges: methods for other aggregation/

fusion/assimilation functions with uncertain data

• Algorithm summary

• Initial guess history + heuristics

• Seek max posterior probability

• Semi- and un-supervised learning

12

High-performance Distributed SMC

• Secure Multi-Party Computation

• Guarantees that computation does

not reveal private input

• Possible approaches

• Shamir’s secret sharing scheme

• Perturbation based

• Homomorphic encryption schemes

• Efficiency (secure sum)

15

Security Schemes – Experiments

• Secure sum protocols in different schemes for nparticipants.

13

DDDAS Software Toolkit

• Scalable and stateless distributed computing

• Small footprint for sensors and field devices

• Low latency, low power communications

• Adopt models/features from FreshBreeze/ROS/HELib

• Deployable at field regional levels, interfaces to traditional

supercomputer simulations

• Algorithm libraries for SMC, distributed computation

• Building block modules (multiplication, division, matrix

inversion)

• Higher level functions (distributed Kalman filter, statistical

summarization, global optimization functions)

• Challenge: robust uncertainty-resilient implementations

adaptively balancing utility (accuracy) and efficiency

Summary

• Next generation DDDAS/Infosymbiotics systems

• Ever expanding platforms – Internet of Things, Smart Systems

• Unified systems/software model for numerous applications

• Requirements and expectations

• Privacy and security – of participants, data, computation

• Uncertainty – resilience to errors, faults, obfuscation, (mis)trust

• Autonomous local and hierarchical analytics, decision makeing

• The PREDICT project

• Feedback driven dynamic management of sensor-participant systems

with privacy protection

• Trust-aware data synthesis, aggregation and validation

• Secure high-performance distributed computing software

Thank you • Acknowledgements

• AFOSR DDDAS FA9550-12-1-0240

• Project team

• Investigators: Li Xiong, Vaidy Sunderam

• Students: Liyue Fan, Slawek Goryczka, Layla Pournjaf, Daniel

Garcia-Ulloa, Xiaofeng Xu

• Project URL

• http://www.mathcs.emory.edu/predict/

AFOSR DDDAS FA9550-12-1-0240